AI is transforming enterprise productivity and reshaping the threat model at the same time. Unlike human users, agentic AI and autonomous agents operate at machine speed and inherit broad network permissions and embedded credentials. This creates new security and compliance risks. CXOs and IT teams must now manage non-human identities that often move laterally, access secrets, or can trigger high-risk infrastructure actions without proper governance controls. 

To stay compliant, organizations must show that their identity and network access controls meet security frameworks defined by NIST, ISO 27001, SOC 2, HIPAA, and GDPR. This increasingly requires isolating AI workloads, vaulting and rotating non-human credentials, auditing privileged elevation, and blocking unauthorized east-west traffic, even when the identity involved is a model or automation agent, not a person. 

Ring-fencing AI workloads addresses these risks by establishing strong identity and network boundaries for them. Ring-fencing is the practice of placing a strict, enforceable boundary around a workload so it can access only what it is explicitly allowed to. For AI, this means creating a distinct identity class for agents and automations, limiting their network visibility, and ensuring any privileged access they receive is temporary and auditable. 

Using the 12Port Platform, organizations can combine privileged access management, a governed credential vault, and microsegmentation to create a controlled execution perimeter for AI workloads. 

How Ring-Fencing AI Improves Compliance 

Security frameworks from NIST and international controls from ISO 27001 emphasize that identity governance applies equally to human, machine, and AI identities. To satisfy auditors, organizations must enforce least privilege, eliminate shadow credentials, distinguish human from machine identity classes, and document boundaries for all AI behavior. Governance must be deterministic, never inferred or improvised by the model.  (see our table below on Compliance Framework Requirements for Identity & Network Controls on AI Workloads) 

Combining privileged access management and microsegmentation makes it possible to comply with security frameworks. Identity-scoped microsegmentation prevents AI workloads from inheriting broad east-west access. Credential vaulting ensures secrets do not live in runtime shells or memory. And Just-In-Time elevation ensures privileged actions expire, require approval, and produce definitive audit trails. 

Establishing a Stronger Security Posture for AI  

AI agents and other machine identities often inherit network visibility simply because of where they run. With 12Port Microsegmentation enforcing network boundaries, east–west traffic is blocked by default. This prevents unauthorized internal scanning, model pivoting, and exploratory behavior that can occur when AI executes instructions literally. Instead of relying on assumptions about “safe” system behavior, organizations get a deterministic perimeter around AI workloads that limits what they can see or touch. 

The 12Port Credential Vault strengthens the identity layer by placing all infrastructure credentials—API keys, service passwords, SSH keys, and DevOps secrets—inside encrypted, governed vaults. This removes secrets from logs, scripts, and runtime environments where AI models frequently encounter them. Any privileged action must go through a Just-In-Time elevation request with human approval and automatic expiration, ensuring access is deliberate and auditable.  

Combined, these controls strengthen your security posture while enabling AI systems to operate effectively within a tight, enforceable security boundary. 

Reducing Operational Risk for Non-Human Identities 

Non-human or machine identities such as AI agents, CI/CD bots, model runners, and service accounts were never designed with access governance in mind. Ring-fencing prevents hallucinated access errors from turning into policy drift or secret disclosure. It reduces long-lived credential sprawl from AI automation. It also reduces prompt-injection blast radius by limiting reachability by identity class. It protects systems without requiring any change to AI agent code. Deterministic policy guardrails create a secure network and identity boundary that AI cannot improvise around. 

12Port makes this process straightforward by classifying AI agents, CI/CD bots, model runners, and service accounts as isolated non-human identities, applying consistent credential governance and network isolation on customer-provided infrastructure — from Windows servers to Linux, ARM, containers, laptops, cloud VMs, and orchestration planes. This creates a single, predictable policy model for both human and machine access without requiring architecture changes or custom wrappers around the models themselves. 

Governance Built for the Era of Agentic AI 

AI identities should not be privileged by assumption. They must be privileged by policy, with boundaries that can be enforced and proven. Ring-fencing provides that foundation, and 12Port delivers  identity controls, credential vaulting, and microsegmentation in one integrated platform required to implement it consistently across a hybrid infrastructure. 

To learn more about ring-fencing using 12Port Platform, book a demo with our experts or download a free trial of the 12Port Platform today. 

Compliance Framework Requirements for Identity & Network Controls on AI Workloads 

The post Ring-fencing AI Workloads for NIST and ISO Compliance  appeared first on 12Port.

*** This is a Security Bloggers Network syndicated blog from 12Port authored by Peter Senescu. Read the original post at: https://www.12port.com/blog/ring-fencing-ai-workloads-for-nist-and-iso-compliance/